The following relates to the information processing arts. The following is described with illustrative reference to image retrieval and categorization applications, but will be useful in numerous other applications entailing comparison, retrieval, categorization, or the like of objects such as images, video content, audio content, and so forth.
Some approaches for comparing objects are disclosed in Liu and Perronnin, U.S. Publ. Appl. No. 2009/0144033 A1 published Jun. 4, 2009, which is incorporated herein by reference in its entirety. In some embodiments disclosed in that reference, a universal mixture model including a plurality of universal mixture model components is adapted to a first object to generate a first object mixture model including a plurality of first object mixture model components having one to one correspondence with the plurality of universal mixture model components. A component-by-component comparison is performed of the plurality of first object mixture model components and a plurality of second object mixture model components obtained by adaptation of the universal mixture model to a second object and having one to one correspondence with the plurality of first object mixture model components. A similarity measure is generated for the first and second objects based on the component by component comparison.
The objects are represented by quantitative representations. For example, an image object can be represented by an unordered set of feature vectors where each feature vector has vector components which are selected quantitative measures of characteristics of an image key patch of the image. By distributing such image key patches across the image (for example on a grid, or randomly, or so forth) and computing a feature vector for each key patch, an unordered set of feature vectors is generated to represent the image. U.S. Publ. Appl. No. 2009/0144033 A1 considers by way of illustrative example image objects which are represented by such unordered sets of feature vectors representing key patches and the universal mixture model is a Gaussian mixture model with Gaussian mixture model components.
The object comparison approach of U.S. Publ. Appl. No. 2009/0144033 A1 has substantial advantages. The universal mixture model provides an a priori starting point for modeling the objects, which enhances speed of estimating the object mixture models. Moreover, since the mixture models for both objects to be compared are generated by adaptation of a same universal mixture model, it follows that there is a one-to-one correspondence of the object mixture model components, enabling component-by-component comparison which again is computationally efficient.
Accordingly, U.S. Publ. Appl. No. 2009/0144033 A1 discloses substantial advancement in the art of object comparison. Nonetheless, still further improvements that enhance computational speed, comparison accuracy, or both, would be desirable. For example, it would be advantageous to increase accuracy of the object comparison by utilizing more complex and probative object models. However, more complex models entail longer computational time and higher memory usage, especially for larger vector sets.